The influence of temperature and precipitation on the movement and use of habitat of wild boar in Switzerland

A subtle subtitle

Abstract

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Introduction

Interactions between different factors can have consequences for population dynamics, species distribution and individual fitness, which are the result of animal movement decisions. Nowadays it is possible to capture spatial behaviour through modern technology such as GPS-System. On one hand, such data give insights into movement patterns on different scales and on the other hand enable a better understanding of the effects of different contexts on spatial behaviour (Morelle et al. 2014: 16).

An important factor, which affects the environmental conditions of animals and therefore the animal movement, is weather (Thurfjell et al. 2014: 467). The weather can influence the mobility, the food availability, or the energetic demands of animals. There are a few studies which analyse the effects of weather on wild boar movement (Thurjfell et al. 2014; Lemel et al. 2003; Morelle et al. 2015).

In this project, we aim to analyse the effects of weather on wild boar movement in the year 2014. According to different studies (Thurfjell et al. 2014; Morelle et al. 2015), wild boar movement is expected to be affected by weather factors. Wild boar is an omnivorous mammal mostly ranging in border zones between pastures and forests (Lemel et al. 2003: 29). In human-dominated landscapes wild boar move less during the day than during the night (Thurfjell et al. 2014: 467). Apart from that cold weather increases the movement in the night, which means a higher demand of energy used (Lemel et al. 2003: 32). In times of snow cover, wild boars decrease their movement duration and distance (Thurfjell et al. 2014: 468). Precipitation increases the movement duration and distance (Morelle et al. 2015: 20). Different studies show that there are effects of weather on wild boar movement. Most of these studies used wild boar movement data from Sweden (Thurjfell et al. 2014; Lemel et al. 2003). Since the wild boar movement data, provided for this project, is from Switzerland, the aim of this project is to analyse the influence of temperature and precipitation on the movement and use of habitat of wild boar in Switzerland. Thus, the research question for this study is: “Is there a seasonal difference in the wild boar moving pattern (clustering vs. moving) but also regarding the location and properties of their habitat?”.

library(ComputationalMovementAnalysisData)
library(ggplot2)
library(dplyr)

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library(tidyverse)

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library(sf)

## Linking to GEOS 3.9.1, GDAL 3.3.2, PROJ 7.2.1; sf_use_s2() is TRUE

library(lubridate)

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library(adehabitatHR)

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library(adehabitatLT)
library(tmap)
library(readr)
tmap_mode("view")

## tmap mode set to interactive viewing

# Include tables with the function "kable"

knitr::kable(head(wildschwein_BE))

TierID	TierName	CollarID	DatetimeUTC	E	N	day	moonilumination
1	Ueli	12272	2014-05-28 21:01:14	2570390	1204820	Tag	0.0033556
1	Ueli	12272	2014-05-28 21:15:18	2570389	1204826	Abenddaemmerung	0.0033556
1	Ueli	12272	2014-05-28 21:30:13	2570391	1204821	Abenddaemmerung	0.0033556
1	Ueli	12272	2014-05-28 21:45:11	2570388	1204826	Abenddaemmerung	0.0033556
1	Ueli	12272	2014-05-28 22:00:33	2570388	1204819	1Nachtviertel	0.0033556
1	Ueli	12272	2014-05-28 22:15:16	2570384	1204828	1Nachtviertel	0.0033556

#loading wildboar data
wildschwein <- wildschwein_BE
wildschwein <- st_as_sf(wildschwein,
                        coords = c("E", "N"), 
                        crs = 2056,
                        remove = FALSE)

Literature Review

Since there are some studies, which already analysed the effects of weather on wild boar movement, it is important to discuss and present the main statements of them. In 1986, Dardaillon conducted a study with wild boar from the Southern France (Dardaillon 1986). The author analysed the seasonal habitat selection and use by wild boars in relation to six habitat types (Dardaillon 1986: 251). In his study, the author discussed with his results that the estimated autumn to spring density is higher than the summer one (Dardaillon 1986: 264). In the summer season, wild boars tend to emigrate to agricultural crops because of what their movement is higher in warm season. Dardaillon does not focus on the effects of weather on wild boar, but this study shows that there are seasonal changes which influence the wild boar movement. Morelle et al. reviewed the literature on wild boar movement ecology with a movement ecology framework. In their review they try to increase the knowledge of the drivers and mechanisms of the spatial behaviour of wild boar (Morelle et al. 2015: 16). In this study, it is stated that to find out where and when the wild boars move external factors, for example seasonality of resources, must be considered (Morelle et al. 2015: 19-20). The study discusses that wild boar can adapt to the seasonality of food resources, where the reduce their movement and home range in masting trees rich areas in autumn (Morelle et al. 2015: 20). Furthermore, Morelle and his colleagues present in a table that wild boar movement decreases at low temperatures, there is increased activity in humid air conditions and snow cover limits the wild boar movement at local and regional levels (Morelle et al. 2015: 23). The study from Thurfjell and his colleagues (2014) discuss the effects of weather on movement of wild boars in Sweden. They used weather data, especially temperature, precipitation, and snow depth. The seasons were divided into five different seasons, where they divided summer into early and late summer (Thurfjell 2014: 468). The results from their study show that wild boar decrease their movement in precipitation during winter and at low temperatures. Wild boar increased their movement in the late summer season. Here one can see that Morelle and his colleagues’ reviews literature about weather effects on wild boar movement stated the same arguments (2015: 20).

Material and Methods

The following analysis is mainly based on the wild boar data set provided by the ZHAW. Between May 2014 and October 2016 18 different wild boars were tracked with GPS collars in the area between Lake Neuchâtel and Lake Biel. Generally, the location was recorded every 15 minutes. In addition to the time stamp and the coordinates, the moonillumination was indicated for every measurement as well but this information was not used in this study.

As the goal of this research is to analyze the influence of weather and season on the movement patterns of wild boar the following additional data was used: - Mean daily temperature [°C] of two weather stations (Neuchâtel and Cressier) - daily precipitation [mm] of the same two weather stations Both datasets were accessed via the IDAweb portal of the Federal Office of Meteorology and Climatology MeteoSwiss.

On ??? Figure ??? one can see the whole study area and the two locations of the weather stations.

For reasons of simplicity, the wild boar data was classified into the meteorological seasons, which always classifies the whole months into one season:

Winter | December, January, February |
Spring | March, April, May |
Summer | June, July, August |
Autumn | September, October, November |

On the weather data some pre-processing was required too. To consider both meteo stations, the mean temperature and precipitation of the two measurements was calculated for every day. After that the weather data frame was joined to the wild boar data frame.

Conceptualization of movement

Is there a seasonal difference in the wild boar moving pattern (clustering vs. moving) To analyse the wild boar moving patterns two different approaches were implemented:

• To get an indicator for the total daily movement, the euclidean distance along all tracking locations per day was calculated for every individual wild boar. Then the total daily distance covered could be opposed to the temperature and precipitation as well as the seasons.
• For the analysis of the amount of clustering that happened per day, the trajectories were segmented, similarly to the segmentation in Exercise 3. For every point the distance to the three preceding and following points were calculated. The mean of these six distances can be seen as the mean step length. Then all mean step lengths were classified as static when they are below the mean step length of all mean step lengths. Then the ratio of static points in all point was calculated. This was then again opposed to the temperature and precipitation data and the season classification like the before described daily movement.

#assigning season to each measurement point
wildschwein <- wildschwein %>% 
  mutate(Month = format(as.Date(DatetimeUTC), "%m"))
  
wildschwein <- wildschwein %>% 
  mutate(season = ifelse(wildschwein$Month == "12" | wildschwein$Month == "01" | wildschwein$Month == "02", "winter",
                    ifelse(wildschwein$Month == "03" | wildschwein$Month == "04" | wildschwein$Month == "05", "spring",
                    ifelse(wildschwein$Month == "06" | wildschwein$Month == "07" | wildschwein$Month == "08", "summer",
                    ifelse(wildschwein$Month == "09" | wildschwein$Month == "10" | wildschwein$Month == "11", "autumn", "no")))))

# plots for presentations
ueli <- filter(wildschwein, TierID == "1")
#sabine <- filter(wildschwein, TierID == "2")
#nicole <- filter(wildschwein, TierID == "5")


#ggplot(ueli, aes(E,N, colour=season)) +
  #geom_point()+
  #ggtitle("Movement of Ueli (ID=1)")

#ggplot(sabine, aes(E,N, colour=season)) +
  #geom_point()+
  #ggtitle("Movement of Sabine (ID=2)")
  
#ggplot(nicole, aes(E,N, colour=season)) +
  #geom_point()+
  #ggtitle("Movement of Nicole (ID=5)")

# plots for presentations
tm_shape(ueli)+
  tm_dots(col="season")

#tm_shape(sabine)+
 # tm_dots(col="season")

#tm_shape(nicole)+
 # tm_dots(col="season")

## Reading the weather data
weatherData <- read_delim("order_103728_data_temp.txt", delim = ";")

## Rows: 2193 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## chr (5): stn, time, tre200d0, qtre200d0, mtre200d0
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

precipData <- read_delim("order_103873_data_precip.txt", delim = ";")

## Rows: 2193 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ";"
## chr (3): stn, time, rka150d0
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Creating additional dataframe, based on the legend.txt for adding
# important info to our dataset

stn <- c("CRM","NEU")
stn_names <- c("Cressier","Neuchâtel")
koord_E <- c(7.067, 6.95)
koord_N <- c(47.05,47.00)
hoehe <- c(430,485) # in meters

legend <- data.frame(stn,stn_names,koord_E,koord_N,hoehe)

# Joining the station and temperature dataframes
data_comb <- full_join(weatherData, legend, by="stn") %>% na.omit()
data_comb <- full_join(data_comb, precipData, by= c("stn","time")) %>% na.omit()

#removing unnecessary columns
data_comb <- subset(data_comb, select = -c(qtre200d0, mtre200d0))

# Converting to an sf object
data_sf <- st_as_sf(data_comb, coords = c("koord_E", "koord_N"))
data_sf <- data_sf %>% 
  mutate(date = ymd(time)) %>% 
  mutate(tre200d0 = as.numeric(tre200d0)) %>% 
  mutate(rka150d0 = as.numeric(rka150d0))

## Warning in mask$eval_all_mutate(quo): NAs durch Umwandlung erzeugt

## Warning in mask$eval_all_mutate(quo): NAs durch Umwandlung erzeugt

data_sf <- data_sf %>% 
  group_by(date) %>% 
  mutate(temp = mean(tre200d0), precip = mean(rka150d0)) %>% 
  st_drop_geometry()

temp_precip <- data_sf %>% 
  filter(stn == "CRM")

wildschwein <- wildschwein %>% 
  mutate(date = as.Date(DatetimeUTC))

wildschwein <- left_join(wildschwein, temp_precip, by = "date")
wildschwein <- subset(wildschwein, select = -c(tre200d0, rka150d0))

ueli <- filter(wildschwein, TierID == "1")
sabine <- filter(wildschwein, TierID == "2")
nicole <- filter(wildschwein, TierID == "5")

ggplot(ueli, aes(E,N, colour=temp)) +
  geom_point()+
  ggtitle("Movement of Ueli (ID=1)")

ggplot(sabine, aes(E,N, colour=temp)) +
  geom_point()+
  ggtitle("Movement of Sabine (ID=2)")

ggplot(nicole, aes(E,N, colour=temp)) +
  geom_point()+
  ggtitle("Movement of Nicole (ID=5)")

ggplot(ueli, aes(E,N, colour=precip)) +
  geom_point()+
  ggtitle("Movement of Ueli (ID=1)")

ggplot(sabine, aes(E,N, colour=precip)) +
  geom_point()+
  ggtitle("Movement of Sabine (ID=2)")

ggplot(nicole, aes(E,N, colour=precip)) +
  geom_point()+
  ggtitle("Movement of Nicole (ID=5)")

#testing segmentation on ueli
ueli <- filter(wildschwein, TierID == "1")
ueli <- ueli %>% 
  mutate(
    stepLength = sqrt((lag(E,1)-E)^2+(lag(E,1)-E)^2),
    stepMean = rowMeans(                       
      cbind(                                   
        sqrt((lag(E,3)-E)^2+(lag(E,3)-E)^2),   
        sqrt((lag(E,2)-E)^2+(lag(E,2)-E)^2),   
        sqrt((lag(E,1)-E)^2+(lag(E,1)-E)^2),   
        sqrt((E-lead(E,1))^2+(E-lead(E,1))^2),  
        sqrt((E-lead(E,2))^2+(E-lead(E,2))^2),
        sqrt((E-lead(E,3))^2+(E-lead(E,3))^2)  
        )                                        
    )
  )

summary(ueli$stepLength)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    0.000    2.816    8.155   36.327   23.901 1535.234        1

ggplot(ueli, aes(stepLength)) +
  geom_histogram(binwidth = 1) +
  geom_vline(xintercept = mean(ueli$stepLength,na.rm = TRUE))

## Warning: Removed 1 rows containing non-finite values (stat_bin).

ueli <- ueli %>%
  mutate(
    static = stepMean < mean(ueli$stepMean,na.rm = TRUE)
  ) 

ueli_dailysummary <- ueli %>% 
  group_by(time, season, temp, precip) %>% 
  summarise(daily_distance = sum(stepLength, na.rm = TRUE),
            ratio = sum(static == TRUE)/(sum(static == TRUE)+ sum(static == FALSE)))

## `summarise()` has grouped output by 'time', 'season', 'temp'. You can override
## using the `.groups` argument.

ueli1 = subset(ueli, time=="20140529")
ggplot(ueli1, aes(x = E, y = N, col = static)) +
  geom_point()+geom_path()

# ueli temperature daily distance
ggplot(ueli_dailysummary, aes(x=temp, y=daily_distance, col = season)) + geom_point()+geom_smooth(method=lm)+labs(title = "scatterplot ueli with trend per season")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 2 rows containing non-finite values (stat_smooth).

## Warning: Removed 2 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=temp, y=daily_distance, col = season)) + geom_point()+labs(title = "scatterplot ueli per season without trend")

## Warning: Removed 2 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=temp, y=daily_distance)) + geom_point()+geom_smooth(method=lm)+labs(title = "scatterplot ueli with overall trend")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Removed 2 rows containing missing values (geom_point).

#ueli temperature static vs movement
ggplot(ueli_dailysummary, aes(x=temp, y=daily_distance)) + geom_point()+labs(title = "scatterplot ueli without trend")

## Warning: Removed 2 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=temp, y=ratio, col = season)) + geom_point()+geom_smooth(method=lm)+labs(title = "scatterplot ueli with trend per season")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 4 rows containing non-finite values (stat_smooth).

## Warning: Removed 4 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=temp, y=ratio, col = season)) + geom_point()+labs(title = "scatterplot ueli per season without trend")

## Warning: Removed 4 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=temp, y=ratio)) + geom_point()+geom_smooth(method=lm)+labs(title = "scatterplot ueli with overall trend")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 4 rows containing non-finite values (stat_smooth).
## Removed 4 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=temp, y=ratio)) + geom_point()+labs(title = "scatterplot ueli without trend")

## Warning: Removed 4 rows containing missing values (geom_point).

#ueli precipitation daily distance
ggplot(ueli_dailysummary, aes(x=precip, y=daily_distance, col = season)) + geom_point()+geom_smooth(method=lm)+labs(title = "scatterplot ueli with trend per season")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 1 rows containing non-finite values (stat_smooth).

## Warning: Removed 1 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=precip, y=daily_distance, col = season)) + geom_point()+labs(title = "scatterplot ueli per season without trend")

## Warning: Removed 1 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=precip, y=daily_distance)) + geom_point()+geom_smooth(method=lm)+labs(title = "scatterplot ueli with overall trend")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Removed 1 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=precip, y=daily_distance)) + geom_point()+labs(title = "scatterplot ueli without trend")

## Warning: Removed 1 rows containing missing values (geom_point).

#ueli precipitation ratio
ggplot(ueli_dailysummary, aes(x=precip, y=ratio, col = season)) + geom_point()+geom_smooth(method=lm)+labs(title = "scatterplot ueli with trend per season")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).

## Warning: Removed 3 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=precip, y=ratio, col = season)) + geom_point()+labs(title = "scatterplot ueli per season without trend")

## Warning: Removed 3 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=precip, y=ratio)) + geom_point()+geom_smooth(method=lm)+labs(title = "scatterplot ueli with overall trend")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Removed 3 rows containing missing values (geom_point).

ggplot(ueli_dailysummary, aes(x=precip, y=ratio)) + geom_point()+labs(title = "scatterplot ueli without trend")

## Warning: Removed 3 rows containing missing values (geom_point).

#ueli movement ratio and seasons
ggplot(ueli_dailysummary, aes(x=season, y = ratio))+
  geom_point()

## Warning: Removed 2 rows containing missing values (geom_point).

# calculating steplength for every wild boar (euclidean distance between sampling points)
wildschwein_steplength <- wildschwein %>% 
  group_by(TierID) %>% 
  mutate(
    stepLength = sqrt((lag(E,1)-E)^2+(lag(E,1)-E)^2),
    stepMean = rowMeans(                       
      cbind(                                   
        sqrt((lag(E,3)-E)^2+(lag(E,3)-E)^2),   
        sqrt((lag(E,2)-E)^2+(lag(E,2)-E)^2),   
        sqrt((lag(E,1)-E)^2+(lag(E,1)-E)^2),   
        sqrt((E-lead(E,1))^2+(E-lead(E,1))^2),  
        sqrt((E-lead(E,2))^2+(E-lead(E,2))^2),
        sqrt((E-lead(E,3))^2+(E-lead(E,3))^2)  
        )                                        
    )
  )

summary(wildschwein_steplength$stepLength)

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
##    0.000    1.421    4.951   34.446   22.184 3600.975       19

#histogram steplength, line == mean
ggplot(wildschwein_steplength, aes(stepLength)) +
  geom_histogram(binwidth = 1) +
  geom_vline(xintercept = mean(wildschwein_steplength$stepLength,na.rm = TRUE))

## Warning: Removed 19 rows containing non-finite values (stat_bin).

#steplength < mean steplength --> static (to be discussed!!)
wildschwein_steplength <- wildschwein_steplength %>%
  mutate(
    static = stepMean < mean(wildschwein_steplength$stepMean,na.rm = TRUE)
  ) 

#calculating daily distance per wild boar --> Achtung: Annahme dass alle regelmässige Messungen über den gesamten Tag haben (wurde noch nicht geprüft)
wildschwein_dailysummary <- wildschwein_steplength %>% 
  group_by(TierID, time, season, temp, precip) %>% 
  summarise(daily_distance = sum(stepLength, na.rm = TRUE),
            ratio = sum(static == TRUE)/(sum(static == TRUE)+ sum(static == FALSE)))

## `summarise()` has grouped output by 'TierID', 'time', 'season', 'temp'. You can
## override using the `.groups` argument.

# all wild boar temperature daily distance
ggplot(wildschwein_dailysummary, aes(x=temp, y=daily_distance, col = season)) + geom_point(size=0.8)+geom_smooth(method=lm)+labs(title = "scatterplot all wildboars with trend per season")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 60 rows containing non-finite values (stat_smooth).

## Warning: Removed 60 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=temp, y=daily_distance, col = season)) + geom_point(size=0.9)+labs(title = "scatterplot all wildboars per season without trend")

## Warning: Removed 60 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=temp, y=daily_distance)) + geom_point(size=0.9)+geom_smooth(method=lm)+labs(title = "scatterplot all wildboars with overall trend")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 60 rows containing non-finite values (stat_smooth).
## Removed 60 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=temp, y=daily_distance)) + geom_point(size=0.9)+labs(title = "scatterplot all wildboars without trend")

## Warning: Removed 60 rows containing missing values (geom_point).

#all wild boar temperature ratio
ggplot(wildschwein_dailysummary, aes(x=temp, y=ratio, col = season)) + geom_point(size=0.8)+geom_smooth(method=lm)+labs(title = "scatterplot all wildboars with trend per season")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 101 rows containing non-finite values (stat_smooth).

## Warning: Removed 101 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=temp, y=ratio, col = season)) + geom_point(size=0.9)+labs(title = "scatterplot all wildboars per season without trend")

## Warning: Removed 101 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=temp, y=ratio)) + geom_point(size=0.9)+geom_smooth(method=lm)+labs(title = "scatterplot all wildboars with overall trend")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 101 rows containing non-finite values (stat_smooth).
## Removed 101 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=temp, y=ratio)) + geom_point(size=0.9)+labs(title = "scatterplot all wildboars without trend")

## Warning: Removed 101 rows containing missing values (geom_point).

#all wild boar precipitation daily distance
ggplot(wildschwein_dailysummary, aes(x=precip, y=daily_distance, col = season)) + geom_point(size=0.8)+geom_smooth(method=lm)+labs(title = "scatterplot all wildboars with trend per season")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 13 rows containing non-finite values (stat_smooth).

## Warning: Removed 13 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=precip, y=daily_distance, col = season)) + geom_point(size=0.9)+labs(title = "scatterplot all wildboars per season without trend")

## Warning: Removed 13 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=precip, y=daily_distance)) + geom_point(size=0.9)+geom_smooth(method=lm)+labs(title = "scatterplot all wildboars with overall trend")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 13 rows containing non-finite values (stat_smooth).
## Removed 13 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=precip, y=daily_distance)) + geom_point(size=0.9)+labs(title = "scatterplot all wildboars without trend")

## Warning: Removed 13 rows containing missing values (geom_point).

#all wild boar precipitation ratio
ggplot(wildschwein_dailysummary, aes(x=precip, y=ratio, col = season)) + geom_point(size=0.8)+geom_smooth(method=lm)+labs(title = "scatterplot all wildboars with trend per season")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 54 rows containing non-finite values (stat_smooth).

## Warning: Removed 54 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=precip, y=ratio, col = season)) + geom_point(size=0.9)+labs(title = "scatterplot all wildboars per season without trend")

## Warning: Removed 54 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=precip, y=ratio)) + geom_point(size=0.9)+geom_smooth(method=lm)+labs(title = "scatterplot all wildboars with overall trend")

## `geom_smooth()` using formula 'y ~ x'

## Warning: Removed 54 rows containing non-finite values (stat_smooth).
## Removed 54 rows containing missing values (geom_point).

ggplot(wildschwein_dailysummary, aes(x=precip, y=ratio)) + geom_point(size=0.9)+labs(title = "scatterplot all wildboars without trend")

## Warning: Removed 54 rows containing missing values (geom_point).

# übersicht temperature und pro tag zurückgelegte distanz für jedes einzelne wildschwein
ggplot(wildschwein_dailysummary, aes(x=temp, y=daily_distance, col = season)) +
  geom_point()+
  facet_wrap(~TierID,labeller = label_both)

## Warning: Removed 60 rows containing missing values (geom_point).

# übersicht precipitation und pro tag zurückgelegte distanz für jedes einzelne wildschwein
ggplot(wildschwein_dailysummary, aes(x=precip, y=daily_distance, col = season)) +
  geom_point()+
  facet_wrap(~TierID,labeller = label_both)

## Warning: Removed 13 rows containing missing values (geom_point).

# einzelner plot pro wildschwein in gross (gleicher input wie oben mit facet_wrap)
#vielleicht sieht man so spannende Muster besser
wild_boars <- c(unique(wildschwein_dailysummary$TierID))

for (i in wild_boars){
  subs <- subset(wildschwein_dailysummary, TierID == i) %>% 
  ggplot(aes(x=temp, y=daily_distance, col=season))+geom_point()+labs(title = "TierID", subtitle = as.character(i))
  print(subs)
}

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 8 rows containing missing values (geom_point).

## Warning: Removed 8 rows containing missing values (geom_point).

## Warning: Removed 16 rows containing missing values (geom_point).

## Warning: Removed 5 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 3 rows containing missing values (geom_point).

## Warning: Removed 6 rows containing missing values (geom_point).

## Warning: Removed 5 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).

## Warning: Removed 2 rows containing missing values (geom_point).

#boxplot daily distance season
ggplot(wildschwein_dailysummary, aes(x = season, y = daily_distance)) + 
  geom_boxplot()

#boxplot movement ratio and seasons
ggplot(wildschwein_dailysummary, aes(x=season, y = ratio))+
  geom_boxplot()

## Warning: Removed 41 rows containing non-finite values (stat_boxplot).

Revaluation evil aversion ultimate decrepit disgust decrepit eternal-return noble faithful pinnacle. Truth ascetic inexpedient decrepit free. Ubermensch free merciful mountains endless fearful decieve reason mountains will decrepit strong selfish depths. Overcome faith snare gains oneself transvaluation.

Results

Philosophy oneself passion play fearful self noble zarathustra deceptions sexuality. Endless ocean of oneself dead ocean. Selfish decrepit.

Discussion

Justice convictions spirit sexuality insofar free marvelous joy. Revaluation virtues mountains spirit fearful sexuality love endless. Society intentions will noble burying aversion moral. Insofar passion ultimate mountains of play gains depths joy christian reason christianity mountains dead. Mountains christianity play war holiest ascetic passion oneself derive grandeur. Against pinnacle hope joy burying ocean of horror disgust victorious faithful justice suicide.

References

Dardaillon, M. (1986): Seasonal Variations in Habitat Selection and Spatial Distribution of Wild Boar (Sus Scrofa) in the Camargue, Southern France. Behavioural Processes, 13, 251-268.

Lemel, J., Truve, J.& Soderberg, B. (2003): Variation in ranging and activity behaviour of European wild boar Sus scrofa in Sweden. Wildl Biol 9, 29–36.Mammal Review, 45, 15-29.

Morelle, K. Podgroski, T., Prévot, C., Keuling, O., Lehaire, F. & Leheune, P. (2014): Towards understanding wild boar Sus scrofa movement: a synthetic movement ecology approach.

Thurfjell, H., Spong, G.& Ericsson, G. (2014): Effects of weather, season, and daylight on female wild boar movement. Acta Theriol, 59, 467-472.